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Efforts in the electronic grocery shopping, i.e. e‐grocery business, focus especially on the physical distribution of the goods. For example, in the USA there are several e‐grocery service providers with various operating concepts and offering various service levels. The home delivery concept of Streamline is based on a reception box at the customer’s garage or home yard enabling unmanned reception. In contrast, WebVan has launched a home delivery concept where the customer can select a convenient half an hour delivery time window. Various service concepts have been implemented and offered, but has anyone really analysed the differences in cost structures of these two and of other concepts in between the two extremes? Investigates existing home delivery service concepts from different angles and presents concrete simulation results of various parameters representing several home delivery service levels. Eventually, identifying the parameters will give guidelines for the future development of the e‐grocery home delivery services.

The service industry is a major component of the economy. Raw material, components, assemblies, and finished products are shipped between suppliers, manufacturers, distributors, and retailers. Accordingly, timely receipt of shipped goods is crucial in maintaining the efficiency and effectiveness of such service processes. A service provider offers an incentive to the customer by specifying a competitive target time for delivery of goods. Further, if the delivery time is deviant from the target value, the provider offers to reimburse the customer for an amount that is proportional to the value of the goods and the degree of deviation from the target value. The service provider may set the price to be charged as a function of product value. This price is in addition to the operational costs of logistics that are not considered in the formulated model. For protection against deviation from target due dates, the service provider agrees to reimburse the customer. The reimbursement could be based on an asymmetric loss function influenced by the degree of deviation from the target due date as well as product value. The penalties could be different for early and late deliveries since the customer may experience different impact and consequences accordingly. The chapter develops a model to determine the amount (price) that the provider should add to the cost estimate of the delivery contract for protection against delivery deviations. Such a cost estimate will include the operational costs (fixed and variable) of the shipment, to which an amount is added to cover the expected payout to customers when the delivery time deviates from the target value. The optimal price should be such that the expected revenue will at least exceed the expected payout.

Original equipment manufacturers and other manufacturing companies rely on the delivery performance of their upstream suppliers to maintain a steady production process. However, supplier capacity uncertainty and delayed delivery often poses a major concern to manufacturers to carry out their production plan as per the desired schedules. The purpose of this paper is to develop a decision model that can improve the delivery performance of suppliers to minimise fluctuations in the supply quantity and the delivery time and thus maximising the performance of the supply chain.

The authors studied a single manufacturer – single supplier supply chain considering supplier uncertain capacity allocation and uncertain time of delivery. Mathematical models are developed to capture expected profit of manufacturer and supplier under this uncertain allocation and delivery behaviour of supplier. A reward–penalty mechanism is proposed to minimise delivery quantity and time of delivery fluctuations from the supplier. Further, an order-fulfilment heuristic based on delivery probability is developed to modify the order quantity which can maximise the probability of a successful deliveries from the supplier.

Analytical results reveal that the proposed reward–penalty mechanism improves the supplier delivery consistency. This consistent delivery performance helps the manufacturer to maintain a steady production schedule and high market share. Modified ordering schedule developed using proposed probability-based heuristic improves the success probability of delivery from the supplier.

Practitioners can benefit from the findings of this study to comprehend how contracts and ordering policy can improve the supplier delivery performance in a manufacturing supply chain.

This paper improves the supplier delivery performance considering both the uncertain capacity allocation and uncertain time of delivery.

Production and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.

In this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.

The optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.

This study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

This research aims to explore the interactions of stakeholders in online food delivery (OFD) platforms, including restaurants, the delivery system and the platform, and the effect on total sales. In order to comprehensively analyze the service of stakeholders, the authors adopted the time duration which provides a unified metric for assessing service performance.

The authors used panel vector autoregressive (PVAR) model analysis to verify the interactions among restaurants, the delivery system, and the platform itself, and to assess their effects on the sales. Data were collected from one of the biggest OFD service platforms in China – Eleme.com.

First, the findings confirmed that the service performance of restaurants, the delivery system and the platform itself influence one another. There is a bidirectional causality between food preparation time (FPT) and actual delivery time (ACDT), as well as between FPT and advance arrival time (ADAT), ACDT and ADAT. Second, the service performance of restaurants, the delivery system, and the platform itself all positively affect sales, thus demonstrating unidirectional causality.

The current research is a pioneering empirical study, as it confirms the dynamic interactions between participants in OFD platforms and the dynamic influence of their respective service performances on sales. The findings of this research have a number of implications for the management and operation of online food ordering and delivery platforms.

The purpose of this paper is to generate a performance model for an order‐to‐delivery (OTD) process in delivery scheduling environments. It aims to do this with a triadic approach, encompassing a customer, a supplier and a logistics service provider.

The paper takes the form of a conceptual analysis and a triadic case study on performance measurement requirements in an OTD process characterized by delivery scheduling, and generating performance models.

Two OTD process performance models, one for the supplier's delivery sub‐process and one for the customer's delivery scheduling, the logistics service provider's transportation and the customer's good receipt sub‐process, in delivery scheduling environments are generated.

A single case study limits the levels of external validity and reliability to analytical generalization.

The generated performance models include definitions of four sub‐processes and outline ten performance dimensions that should be of relevance for several companies to apply.

This is the first approach that generates performance models for a triadic OTD process for use in delivery scheduling environments.

The present work attempts to determine an appropriate number of different categories of Delivery Persons for a Hyper-local Food Delivery Organization for different intervals within a day and across days within a week which would provide a satisfactory level of service to the target customers and at the same time would become cost-efficient.

Currently the firm estimates the required number of Delivery Persons for “lunch peak” and “dinner peak” of the next week's weekdays and weekend based on the maximum number of orders occurring during the same period of both weekdays and weekend in the current week. The proposed approach involves determining the projected demand in every four-hourly interval of both week-days and weekend in the next week. Subsequently, the study has developed a simple integer programming model for determining the optimum number of Delivery Persons based on the projected demand data.

The existing approach followed by the firm indicates that the Delivery Persons remain unutilized during periods of low demand. The proposed model demonstrated savings to the tune of 21.4% in manpower cost without any erosion in the service level.

The study has made three tangible contributions. First, the development of a simple methodology for estimating the demand of next period allows the Managers to utilize dynamic demand data. Second, the development of a simple integer programming model helps managers determine an appropriate number of Delivery Persons in different intervals in both weekdays and weekend. Third, the development of a framework of hiring strategy aids managers in adopting a particular hiring strategy under a particular context keeping in mind the magnitude of demand for food, demand for delivery service and the cost of providing the service.

This research seeks to identify and apply techniques that can be used in a supply chain context to diagnose the causes of variability in delivery lead time.

A literature review was conducted and a number of quality management (QM), techniques were selected as candidates for diagnosing delivery time variability. A case study of the application of these techniques is provided on the UK‐based defence supply chain that supported UK operations in the Iraq war of 2003.

Candidate QM techniques for diagnosing delivery time variability were identified, namely: Process Chart; Histogram; Failure Mode and Effect Analysis; and Cause and Effect Analysis. These techniques were successful in enabling the diagnosis of the causes of delivery time variability in the context of the case study investigated.

The work illustrates how QM techniques can be employed to address issues with supply chains, not least with regard to the important problem of variability in delivery leadtime. In practice, this highlights benefits that result to practitioners in order to improve the performance of operations in a dynamic setting, such as the defence supply chain studied here.

This work has value in presenting the findings of an in‐depth case study on the application of QM techniques in a multi‐echelon supply chain setting. It is also original in employing the FMEA technique together with an end‐customer perspective to assess the effect of failure modes in operations across a supply chain. FMEA also provided the means to examine supply chain risk, thus providing a research instrument for deploying risk as a lens. The application of QM techniques in this novel setting provides support for their application beyond the conventional setting of internal operations.

The effects of cellular manufacturing (CM) on increased delivery speed and resource utilization along with its interaction with volume, mix, routing, and labor flexibilities are not clear for manufacturers and supply chain managers. Aims to focus on this.

Based on real data from a US screen‐printing company, a simulation model is designed to determine the influence of volume, mix, routing and labor flexibilities in presence of volatile demand. Simulation of one and two cell configuration is compared with job shop to determine the shortest delivery and highest utilization.

As volume flexibility increases, delivery is faster in presence of CM compared to job shop. Furthermore, added routing flexibility results in 70 percent shorter lead time with low volume flexibility, and 85 percent shorter lead time with high volume flexibility. Additionally, in the two‐cell design, assignment of fewer, but more multi‐skilled workers shared between cells results in higher utilization and lower lead time.

This study contributes to the manufacturing research by revealing the benefits of CM, and the importance of volume, routing, and labor flexibilities reacting quickly to volatile demand in today's dispersed manufacturing environment. Also, this study demonstrates that labor allocation is equally important in manufacturing cells as the equipment and part decisions are.

The findings provide manufacturers a guideline on how to best set up CM and operational flexibilities to respond faster to volatile demand. The simulation model is successful in showing that cells and manufacturing flexibilities are strong enablers of faster delivery lead time and higher resource utilization.

Key components of the logistics mix are described in an effort to create an understanding of the total logistics concept. Chapters include an introduction to logistics; the strategic role of logistics, customer service levels, channel relationships, facilities location, transport, inventory management, materials handling, interface with production, purchasing and materials management, estimating demand, order processing, systems performance, leadership and team building, business resource management.